At RSNA 2016 (Booth #6143) RADLogics will showcase a solution that delivers long-awaited leaps in quality and productivity for radiologists. The demonstration shows CT and Xray imaging studies uploaded to the RADLogics machine learning image analysis AlphaPoint software platform. Findings are measured and characterized, and a preliminary report is automatically created within the radiologist’s familiar reporting template—including key images—within minutes.

“The service provides the computational equivalent of a medical resident that traditionally prepares preliminary findings for radiologists in academic medical centers,” says Moshe Becker, executive chairman and co-founder, RADLogics. “Our Virtual Resident™ is capable of performing this function for more studies and at a higher volume of images in a fraction of the time. The service also preserves the existing workflow, while enabling radiologists to handle more images per study and more studies per day while actually improving quality of care.”

The RADLogics cloud-based AlphaPoint solution uses machine learning image analysis and advanced big data analytics to search and analyze imaging data associated with CTs, MRIs and X-rays. The speed and accuracy of the machine learning technology is constantly improved by RADLogics radiology knowledgebase, which is continuously updated with studies from radiology departments and imaging centers around the world.

In addition to the speed and productivity improvements, the cloud-based service also removes variability in how different radiologists interpret studies. It provides objective measurements and characterization of findings, which means greater consistency in comparing studies for the same patient tracked over a period of time.

About RADLogics
RADLogics’ mission is to use machine learning to help radiologists provide higher value reports to better serve referring physicians and patients. The company provides one of the first commercial machine learning image analysis solutions for radiologists. The AlphaPoint platform uses proprietary algorithms that are constantly improved based on a knowledgebase of real clinical studies from hospitals around the world. The technology makes it possible to process enormous amounts of imaging data in seconds, and deliver a preliminary report to the radiologist’s workstation, flowed into the reporting template and PACS that the radiologist already uses. RADLogics refers to this capability as Virtual Resident.